A Large-Scale Comparative Analysis of Imputation Methods for Single-Cell RNA Sequencing Data

A team of researchers led by Yuichiro Iwashita has published a landmark comparative analysis on arXiv, offering the most comprehensive benchmark to date for single-cell RNA sequencing (scRNA-seq) data imputation. The study rigorously evaluated 15 different imputation methods spanning 7 methodological categories—from traditional statistical models to cutting-edge deep learning (DL) techniques like diffusion-based and GAN-based models. The evaluation was conducted across a massive scale of 30 datasets sourced from 10 different experimental protocols, assessing performance on 6 distinct downstream analytical tasks. This scope far exceeds previous, more limited benchmarking efforts.

The results delivered a significant and perhaps counterintuitive finding: traditional imputation methods, including model-based, smoothing-based, and low-rank matrix completion techniques, generally outperformed modern DL-based approaches. The study also revealed a critical nuance: strong performance in numerically recovering gene expression values does not guarantee improved biological interpretability in subsequent analysis. Furthermore, the research underscores that imputation performance is highly context-dependent, varying substantially across datasets, experimental protocols, and specific analytical goals. Consequently, the paper concludes that no single method is universally superior, providing a vital, evidence-based framework for researchers to select the optimal tool for their specific biological question and data type.